# GRADER_ONLY
autograded_result = {
    "netid": None,
    "order": [],
    "student_score": 0,
    "total_available": 0,
    "breakdown": {}
}

def record_part(part_name, did_pass, available_points, message=""):
    global autograded_result
    
    # If already graded, remove previous graded order entry for this part
    if part_name in autograded_result["order"]:
        print(f"[Warning] Autograder: {part_name} already graded or a duplicate key exists")
#         autograded_result["order"].remove(part_name)
    
    # Record graded order
    autograded_result["order"].append(part_name)
    
    # Add graded result for this part
    autograded_result["breakdown"][part_name] = {
        "available_points": available_points,
        "points": available_points if did_pass else 0,
        "did_pass": did_pass,
        "message": message,
    }
    
    # Recalculate total_points and total_available points
    autograded_result["student_score"] = 0
    autograded_result["total_available"] = 0
    
    for part_info in autograded_result["breakdown"].values():
        autograded_result["student_score"] += part_info["points"]
        autograded_result["total_available"] += part_info["available_points"]
    
    print(autograded_result)

Anaylze and Visualize Chicago Uber/Lyft Trips

• 🏆 80 points available
• 🤠 Author: Ye Joo Park (ypark32@illinois.edu)
• ✏️ Last updated on 11/07/2021

💎 Case overview

There have been four Transportation Network Providers (often called rideshare companies 🚗) licensed to operate in Chicago. These rideshare companies are required to routinely report vehicles, drivers, and trips information to the City of Chicago, which are published to the Chicago Data Portal. The latest trips dataset can be downloaded at this page.

🐢 Your dataset

• The original dataset contains information about 170 million ridesharing trips between 2018 and 2021. That translates to over 40GB of raw data. 🙀☠️🙀
• Approximately 1% of the original data has been randomly sampled, preprocessed, and compressed.
• Only 2019 and 2020 data have been selected.

⚔️ Your goal

Analyze the dataset and answer the following questions:

• 👉 How has the COVID-19 affected the total number of trips in 2019 and 2020?
• 👉 How has the COVID-19 affected the number of monthly trips?
• 👉 Have passengers become more generous or frugal when tipping during the pandemic?
• 👉 What were the trip dimensions (trip duration, distance, and fare) of rides taken place right before the July 4th fireworks in 2019 and 2020?
• 👉 Are passengers pooling rides during the pandemic?
• 👉 What are the top 20 pickup areas (by volume)?
• 👉 What are the average trip total for each of the top 20 pickup area?
• 👉 Is there any area that has a unusally large trip total?

---

▶️ Run the code cell below to import unittest, a module used for 🧭 Check Your Work sections and the autograder.

# DO NOT MODIFY THE CODE IN THIS CELL
import base64
import unittest
tc = unittest.TestCase()

assignment_name = "case-study-03-ridesharing-trips-dataviz"
is_autograder_env = 'record_part' in globals().keys()

if is_autograder_env:
    autograded_result["assignment_name"] = assignment_name

🎯 Enter your NetID

• 👇 Fill in the value of netid with your NetID below.
  • This value will be used by the autograder.

netid = ""

print(f"Your NetID is {netid}.")

Your NetID is .

# GRADER_ONLY
# This code will be stripped to the student-distributed version
netid = "grader_netid"

🧭 Check Your NetID

If the code cell below doesn't throw an error, you're ready to begin this assignment.

# DO NOT CHANGE THE CODE BELOW
tc.assertIsNotNone(netid)
tc.assertIsInstance(netid, str)
tc.assertNotEqual(netid.strip(), "", "Did you forget to enter your NetID?")
tc.assertFalse(netid.strip().isdecimal(), "Did you enter your UIN instead of your NetID?")

if is_autograder_env:
    autograded_result["netid"] = netid

---

🔨 Import packages and dataset

▶️ Run the code below to ensure you're using the correct version of plotly.

# Install plotly 5.3.1 using pip
# Colab environment supports pip
if 'google.colab' in str(get_ipython()):
    !pip install plotly==5.3.1

# If you're using conda, use the code below
# !conda install -c plotly plotly=5.3.1

▶️ Run the code cell below to import packages used in the case.

import pandas as pd
import numpy as np
import plotly
import plotly.express as px
import plotly.graph_objects as go

# plotly.io is a low-level interface for interacting with figures/
# plotly.io.templates lists available plotly templates
# https://plotly.com/python-api-reference/plotly.io.html
import plotly.io as pio

pd.set_option('display.max_columns', 50)

🧭 Check Plotly Version

Run the code below to ensure that your notebook uses the same Plotly version as the autograder.

# DO NOT CHANGE THE CODE IN THIS CELL
print(f'The current plotly version is {plotly.__version__}')
tc.assertTrue(plotly.__version__.startswith('5.3'), 'Your plotly version should be 5.3.x')

The current plotly version is 5.3.1

▶️ Run the code below to import and process the trips dataset.

df = pd.read_csv(
    'https://github.com/bdi475/datasets/raw/main/case-studies/chicago-ridesharing/chicago-ridesharing-trips-2019-2020.csv.gz',
    compression='gzip',
    parse_dates=['start']
)
df_community_areas = pd.read_csv('https://github.com/bdi475/datasets/raw/main/case-studies/chicago-ridesharing/chicago-community-area-numbers.csv')

# Replace community area numbers with area names
df = df.merge(df_community_areas, left_on='pickup_area', right_on='area_number', how='left')
df['pickup_area'] = df['community'].copy()
df.drop(columns=['community', 'area_number'], inplace=True)

df = df.merge(df_community_areas, left_on='dropoff_area', right_on='area_number', how='left')
df['dropoff_area'] = df['community'].copy()
df.drop(columns=['community', 'area_number'], inplace=True)

# Create a copy for 🧭 Check Your Work section
df_backup = df.copy()

---

📐 Part 1: Data overview

---

🎯 Deliverable 1: First 5 rows

👇 Tasks

• ✔️ Display the first 5 rows of df using .head().

🚀 Hint

my_dataframe.head()

# YOUR CODE BEGINS
df.head(5)
# YOUR CODE ENDS

	start	trip_seconds	trip_miles	pickup_area	dropoff_area	fare	tip	additional_charges	trip_total	shared_trip_authorized	trips_pooled	pickup_lat	pickup_lon	dropoff_lat	dropoff_lon
0	2019-01-01	303	1.4	Garfield Ridge	Clearing	5.0	5.0	7.68	17.68	False	1	41.792592	-87.769615	41.779583	-87.768511
1	2019-01-01	697	3.0	Near North Side	Near West Side	7.5	0.0	2.50	10.00	False	1	41.892073	-87.628874	41.885300	-87.642808
2	2019-01-01	1598	4.7	Lincoln Park	Loop	10.0	2.0	2.50	14.50	False	1	41.922083	-87.634156	41.870607	-87.622173
3	2019-01-01	573	0.9	Near North Side	Near North Side	5.0	0.0	2.50	7.50	False	1	41.892042	-87.631864	41.892508	-87.626215
4	2019-01-01	1562	2.4	Near North Side	Near North Side	10.0	0.0	2.50	12.50	False	1	41.900221	-87.629105	41.895033	-87.619711

🧭 Check your work

• Once you're done, run the code cell below to test correctness.
• ✔️ If the code cell runs without an error, you're good to move on.
• ❌ If the code cell throws an error, go back and fix incorrect parts.

# DO NOT CHANGE THE CODE IN THIS CELL
part_name = "deliverable-01"
did_pass = True
available_points = 2
message = ""

try:
    pass
except Exception as err:
    did_pass = False
    message = err
    
    if not is_autograder_env:
        raise err
finally:
    if is_autograder_env:
        record_part(part_name, did_pass, available_points, message)

{'netid': 'grader_netid', 'order': ['deliverable-01'], 'student_score': 2, 'total_available': 2, 'breakdown': {'deliverable-01': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}}, 'assignment_name': 'case-study-03-ridesharing-trips-dataviz'}

---

🎯 Deliverable 2: Summary of DataFrame

👇 Tasks

• ✔️ Print a concise summary of df using .info().

🚀 Hint

my_dataframe.info()

# YOUR CODE BEGINS
df.info()
# YOUR CODE ENDS

<class 'pandas.core.frame.DataFrame'>
Int64Index: 1388193 entries, 0 to 1388192
Data columns (total 15 columns):
 #   Column                  Non-Null Count    Dtype         
---  ------                  --------------    -----         
 0   start                   1388193 non-null  datetime64[ns]
 1   trip_seconds            1388193 non-null  int64         
 2   trip_miles              1388193 non-null  float64       
 3   pickup_area             1388193 non-null  object        
 4   dropoff_area            1388193 non-null  object        
 5   fare                    1388193 non-null  float64       
 6   tip                     1388193 non-null  float64       
 7   additional_charges      1388193 non-null  float64       
 8   trip_total              1388193 non-null  float64       
 9   shared_trip_authorized  1388193 non-null  bool          
 10  trips_pooled            1388193 non-null  int64         
 11  pickup_lat              1388193 non-null  float64       
 12  pickup_lon              1388193 non-null  float64       
 13  dropoff_lat             1388193 non-null  float64       
 14  dropoff_lon             1388193 non-null  float64       
dtypes: bool(1), datetime64[ns](1), float64(9), int64(2), object(2)
memory usage: 160.2+ MB

# DO NOT CHANGE THE CODE IN THIS CELL
part_name = "deliverable-02"
did_pass = True
available_points = 2
message = ""

try:
    pass
except Exception as err:
    did_pass = False
    message = err
    
    if not is_autograder_env:
        raise err
finally:
    if is_autograder_env:
        record_part(part_name, did_pass, available_points, message)

{'netid': 'grader_netid', 'order': ['deliverable-01', 'deliverable-02'], 'student_score': 4, 'total_available': 4, 'breakdown': {'deliverable-01': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-02': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}}, 'assignment_name': 'case-study-03-ridesharing-trips-dataviz'}

---

🎯 Deliverable 3: Number of rows and columns in the dataset

👇 Tasks

• ✔️ Store the number of rows in df to a new variable named num_rows.
• ✔️ Store the number of columns in df to a new variable named num_cols.
• ✔️ Both num_rows and num_cols must be ints.
• ✔️ Use .shape, not len().

# YOUR CODE BEGINS
num_rows = df.shape[0]
num_cols = df.shape[1]
# YOUR CODE ENDS

print(f'There are {num_rows} rows and {num_cols} columns in the dataset.')

There are 1388193 rows and 15 columns in the dataset.

🧭 Check Your Work

• Once you're done, run the code cell below to test correctness.
• ✔️ If the code cell runs without an error, you're good to move on.
• ❌ If the code cell throws an error, go back and fix incorrect parts.

# DO NOT CHANGE THE CODE IN THIS CELL
part_name = "deliverable-03"
did_pass = True
available_points = 2
message = ""

try:
    tc.assertEqual(num_rows, len(df_backup.index), f'Number of rows should be {len(df_backup.index)}')
    tc.assertEqual(num_cols, len(df_backup.columns), f'Number of columns should be {len(df_backup.columns)}')
except Exception as err:
    did_pass = False
    message = err
    
    if not is_autograder_env:
        raise err
finally:
    if is_autograder_env:
        record_part(part_name, did_pass, available_points, message)

{'netid': 'grader_netid', 'order': ['deliverable-01', 'deliverable-02', 'deliverable-03'], 'student_score': 6, 'total_available': 6, 'breakdown': {'deliverable-01': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-02': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-03': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}}, 'assignment_name': 'case-study-03-ridesharing-trips-dataviz'}

---

🗓️ Part 2: Extract datetime values into separate columns

The start column contains trip start timestamps. In this part of the case study, you will extract year, month, day of the month, day of the week, hour, and weekday/weekend information into separate columns.

---

🎯 Deliverable 4: Extract year into a new column

▶️ Run the code below to print the first 3 values of the start column and the data types.

display(df['start'].head(3))
print('=================================================')
print(f"df['start'] column's data type is {df['start'].dtype}.")

0   2019-01-01
1   2019-01-01
2   2019-01-01
Name: start, dtype: datetime64[ns]

=================================================
df['start'] column's data type is datetime64[ns].

👇 Tasks

• ✔️ From df, extract the year (e.g., 2019, 2020) from the start column and store it to a new column named year.
• ✔️ We will provide you the fully-working code below.

🔥 Solution

👆 date_series.dt is an accessor object datetimelike Series values. You can refer to the documentation here.

# YOUR CODE BEGINS
df['year'] = df['start'].dt.year
# YOUR CODE ENDS

display(df[['start', 'year']].sample(5))

	start	year
491424	2019-07-02 11:00:00	2019
9852	2019-01-05 13:15:00	2019
1036018	2020-01-29 14:15:00	2020
372753	2019-05-18 15:15:00	2019
620176	2019-08-20 20:00:00	2019

🧭 Check Your Work

• Once you're done, run the code cell below to test correctness.
• ✔️ If the code cell runs without an error, you're good to move on.
• ❌ If the code cell throws an error, go back and fix incorrect parts.

# DO NOT CHANGE THE CODE IN THIS CELL
part_name = "deliverable-04"
did_pass = True
available_points = 2
message = ""

try:
    df_backup['year'] = df_backup['start'].dt.year

    tc.assertEqual(df.shape, df_backup.shape, 'Incorrect number of rows and/or columns')
    pd.testing.assert_frame_equal(
        df[['start', 'year']].sort_values('start').reset_index(drop=True),
        df_backup[['start', 'year']].sort_values('start').reset_index(drop=True),
    )
except Exception as err:
    did_pass = False
    message = err
    
    if not is_autograder_env:
        raise err
finally:
    if is_autograder_env:
        record_part(part_name, did_pass, available_points, message)

{'netid': 'grader_netid', 'order': ['deliverable-01', 'deliverable-02', 'deliverable-03', 'deliverable-04'], 'student_score': 8, 'total_available': 8, 'breakdown': {'deliverable-01': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-02': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-03': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-04': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}}, 'assignment_name': 'case-study-03-ridesharing-trips-dataviz'}

---

🎯 Deliverable 5: Extract month, day of month, day of week, and hour into columns

👇 Tasks

• ✔️ Similar to the previous deliverable, extract the month, day of month, day of week, and hour of each start time in df to new columns.
• ✔️ Use the following column names. Sample values are also given.
  • month: 1, 2, ..., 11, 12
  • day: 1, 2, ..., 28, 29, 30, 31
  • dayofweek: 0 for Monday, 6 for Sunday
  • hour: 0, 1, ..., 22, 23

# YOUR CODE BEGINS
df['month'] = df['start'].dt.month
df['day'] = df['start'].dt.day
df['dayofweek'] = df['start'].dt.dayofweek
df['hour'] = df['start'].dt.hour
# YOUR CODE ENDS

display(df[['start', 'month', 'day', 'dayofweek', 'hour']].sample(5))

	start	month	day	dayofweek	hour
681497	2019-09-14 11:45:00	9	14	5	11
378421	2019-05-20 13:30:00	5	20	0	13
710541	2019-09-25 17:00:00	9	25	2	17
1360766	2020-11-28 13:15:00	11	28	5	13
522507	2019-07-14 14:00:00	7	14	6	14

🧭 Check Your Work

• Once you're done, run the code cell below to test correctness.
• ✔️ If the code cell runs without an error, you're good to move on.
• ❌ If the code cell throws an error, go back and fix incorrect parts.

# DO NOT CHANGE THE CODE IN THIS CELL
part_name = "deliverable-05"
did_pass = True
available_points = 4
message = ""

try:
    decoded_code = base64.b64decode(b'ZGZfYmFja3VwWydtb250aCddID0gZGZfYmFja3VwWydzd\
    GFydCddLmR0Lm1vbnRoCmRmX2JhY2t1cFsnZGF5J10gPSBkZl9iYWNrdXBbJ3N0YXJ0J10uZHQuZG\
    F5CmRmX2JhY2t1cFsnZGF5b2Z3ZWVrJ10gPSBkZl9iYWNrdXBbJ3N0YXJ0J10uZHQuZGF5b2Z3ZWV\
    rCmRmX2JhY2t1cFsnaG91ciddID0gZGZfYmFja3VwWydzdGFydCddLmR0LmhvdXI=')
    eval(compile(decoded_code, '<string>', 'exec'))

    tc.assertEqual(df.shape, df_backup.shape, 'Incorrect number of rows and/or columns')
    pd.testing.assert_frame_equal(
        df[['start', 'month', 'day', 'dayofweek', 'hour']].sort_values('start').reset_index(drop=True),
        df_backup[['start', 'month', 'day', 'dayofweek', 'hour']].sort_values('start').reset_index(drop=True),
    )
except Exception as err:
    did_pass = False
    message = err
    
    if not is_autograder_env:
        raise err
finally:
    if is_autograder_env:
        record_part(part_name, did_pass, available_points, message)

{'netid': 'grader_netid', 'order': ['deliverable-01', 'deliverable-02', 'deliverable-03', 'deliverable-04', 'deliverable-05'], 'student_score': 12, 'total_available': 12, 'breakdown': {'deliverable-01': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-02': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-03': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-04': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-05': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}}, 'assignment_name': 'case-study-03-ridesharing-trips-dataviz'}

---

🎯 Deliverable 6: Create weekday_weekend column

👇 Tasks

• ✔️ Assume Mondays-Thursdays are weekdays and Fridays-Sundays are weekends.
• ✔️ Create a new column named weekday_weekend in df.
• ✔️ For each row, the value of weekday_weekend will either be string 'weekday' or string 'weekend' (case-sensitive) based on the value of the dayofweek column.
  • 'weekday' if dayofweek is less than or equal to 3 (0 == Monday, 1 == Tuesday, 2 == Wednesday, 3 == Thursday)
  • 'weekend' if otherwise (4 == Friday, 5 == Saturday, 6 == Sunday)

🚀 Hint

There are many ways to achieve this task.

The code below creates a new column named cheap_expensive where the value will be string 'cheap' if the price is less than or equal to 10 and 'expensive' if otherwise.

my_dataframe['cheap_expensive'] = np.where(my_dataframe['price'] <= 10, 'cheap', 'expensive')

# YOUR CODE BEGINS
df['weekday_weekend'] = np.where(df['dayofweek'] <= 3, 'weekday', 'weekend')
# YOUR CODE ENDS

display(df[['dayofweek', 'weekday_weekend']].sample(5))

	dayofweek	weekday_weekend
400722	2	weekday
1158969	0	weekday
773554	5	weekend
724987	0	weekday
796975	6	weekend

🧭 Check Your Work

• Once you're done, run the code cell below to test correctness.
• ✔️ If the code cell runs without an error, you're good to move on.
• ❌ If the code cell throws an error, go back and fix incorrect parts.

# DO NOT CHANGE THE CODE IN THIS CELL
part_name = "deliverable-06"
did_pass = True
available_points = 3
message = ""

try:
    decoded_code = base64.b64decode(b'ZGZfYmFja3VwWyd3ZWVrZGF5X3dlZWtlbmQnXSA9IG5wL\
    ndoZXJlKGRmX2JhY2t1cFsnZGF5b2Z3ZWVrJ10gPD0gMywgJ3dlZWtkYXknLCAnd2Vla2VuZCcp')
    eval(compile(decoded_code, '<string>', 'exec'))

    tc.assertEqual(df.shape, df_backup.shape, 'Incorrect number of rows and/or columns')
    pd.testing.assert_frame_equal(
        df[['start', 'dayofweek', 'weekday_weekend']].sort_values('start').reset_index(drop=True),
        df_backup[['start', 'dayofweek', 'weekday_weekend']].sort_values('start').reset_index(drop=True),
    )
except Exception as err:
    did_pass = False
    message = err
    
    if not is_autograder_env:
        raise err
finally:
    if is_autograder_env:
        record_part(part_name, did_pass, available_points, message)

{'netid': 'grader_netid', 'order': ['deliverable-01', 'deliverable-02', 'deliverable-03', 'deliverable-04', 'deliverable-05', 'deliverable-06'], 'student_score': 15, 'total_available': 15, 'breakdown': {'deliverable-01': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-02': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-03': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-04': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-05': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}, 'deliverable-06': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}}, 'assignment_name': 'case-study-03-ridesharing-trips-dataviz'}

---

😷 Part 3: Visualize the effects of COVID-19 on the volume of ridesharing trips

Although the first case of COVID-19 was reported in January 2020 in the United States, people started to take it seriously in March 2020.

How did COVID-19 affect the volume of ridesharing trips? 💨 Let's visualize and compare the monthly number of trips for both 2019 and 2020.

---

🎯 Deliverable 7: Total number of trips in 2019 and 2020

👇 Tasks

• ✔️ Using df, count the number of trips made in 2019 and store the number to a new variable named num_2019_trips.
• ✔️ Using df, count the number of trips made in 2020 and store the number to a new variable named num_2020_trips.

🚀 Hint

For num_2019_trips, retrieve the number of rows where df['year'] is 2019.

# YOUR CODE BEGINS
num_2019_trips = (df['year'] == 2019).sum()
num_2020_trips = (df['year'] == 2020).sum()
# YOUR CODE ENDS

print(f'There were {num_2019_trips} trips in 2019.')
print(f'There were {num_2020_trips} trips in 2020.')
print(f'The number of trips decreased by {(num_2019_trips - num_2020_trips) / num_2019_trips * 100:.1f}%.')

There were 966346 trips in 2019.
There were 421847 trips in 2020.
The number of trips decreased by 56.3%.

🧭 Check Your Work

• Once you're done, run the code cell below to test correctness.
• ✔️ If the code cell runs without an error, you're good to move on.
• ❌ If the code cell throws an error, go back and fix incorrect parts.

# DO NOT CHANGE THE CODE IN THIS CELL
part_name = "deliverable-07"
did_pass = True
available_points = 3
message = ""

try:
    decoded_code = base64.b64decode(b'bnVtXzIwMTlfdHJpcHNfY2hlY2sgPSBkZ\
    l9iYWNrdXBbJ3llYXInXS52YWx1ZV9jb3VudHMoKS5sb2NbMjAxOV0KbnVtXzIwMjBf\
    dHJpcHNfY2hlY2sgPSBkZl9iYWNrdXBbJ3llYXInXS52YWx1ZV9jb3VudHMoKS5sb2NbMjAyMF0=')
    eval(compile(decoded_code, '<string>', 'exec'))

    tc.assertEqual(num_2019_trips, num_2019_trips_check,
                   f"Incorrect number of 2019 trips, should be {num_2019_trips_check}")
    tc.assertEqual(num_2020_trips, num_2020_trips_check,
                   f"Incorrect number of 2020 trips, should be {num_2020_trips_check}")
except Exception as err:
    did_pass = False
    message = err
    
    if not is_autograder_env:
        raise err
finally:
    if is_autograder_env:
        record_part(part_name, did_pass, available_points, message)

{'netid': 'grader_netid', 'order': ['deliverable-01', 'deliverable-02', 'deliverable-03', 'deliverable-04', 'deliverable-05', 'deliverable-06', 'deliverable-07'], 'student_score': 18, 'total_available': 18, 'breakdown': {'deliverable-01': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-02': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-03': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-04': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-05': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}, 'deliverable-06': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-07': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}}, 'assignment_name': 'case-study-03-ridesharing-trips-dataviz'}

---

🎯 Deliverable 8: Monthly number of trips and tip percentage

👇 Tasks

• ✔️ Using df, calculate the number of monthly trips and average tip percentages.
• ✔️ Use the code given below.

# YOUR CODE BEGINS
df_monthly = df.groupby(['year', 'month'], as_index=False).agg({
    'start': 'count',
    'tip': 'sum',
    'fare': 'sum',
}).rename(columns={
    'start': 'num_trips'
})

# Calculate average tip %
df_monthly['tip_pct'] = df_monthly['tip'] / df_monthly['fare']

# Remove tip and fare columns since they're not needed anymore
df_monthly.drop(columns=['tip', 'fare'], inplace=True)
# YOUR CODE ENDS

display(df_monthly)

	year	month	num_trips	tip_pct
0	2019	1	77313	0.048254
1	2019	2	77374	0.045353
2	2019	3	89688	0.047561
3	2019	4	79829	0.047786
4	2019	5	84168	0.049438
5	2019	6	80349	0.052670
6	2019	7	79007	0.055360
7	2019	8	81141	0.056450
8	2019	9	76516	0.058380
9	2019	10	82129	0.056311
10	2019	11	79395	0.052962
11	2019	12	79437	0.054570
12	2020	1	76255	0.052041
13	2020	2	78726	0.050506
14	2020	3	45565	0.045580
15	2020	4	12612	0.028806
16	2020	5	15618	0.037591
17	2020	6	22092	0.046910
18	2020	7	28129	0.042972
19	2020	8	29010	0.037146
20	2020	9	30265	0.040097
21	2020	10	33488	0.040802
22	2020	11	24501	0.040884
23	2020	12	25586	0.040130

🧭 Check Your Work

• Once you're done, run the code cell below to test correctness.
• ✔️ If the code cell runs without an error, you're good to move on.
• ❌ If the code cell throws an error, go back and fix incorrect parts.

# DO NOT CHANGE THE CODE IN THIS CELL
part_name = "deliverable-08"
did_pass = True
available_points = 2
message = ""

try:
    decoded_code = base64.b64decode(b'ZGZfbW9udGhseV9jaGVjayA9IGRmX2JhY2t1cC5ncm91\
    cGJ5KFsneWVhcicsICdtb250aCddLCBhc19pbmRleD1GYWxzZSkuYWdnKHsKICAgICdzdGFydCc6IC\
    djb3VudCcsCiAgICAndGlwJzogJ3N1bScsCiAgICAnZmFyZSc6ICdzdW0nLAp9KS5yZW5hbWUoY29s\
    dW1ucz17CiAgICAnc3RhcnQnOiAnbnVtX3RyaXBzJwp9KQoKZGZfbW9udGhseV9jaGVja1sndGlwX3\
    BjdCddID0gZGZfbW9udGhseV9jaGVja1sndGlwJ10gLyBkZl9tb250aGx5X2NoZWNrWydmYXJlJ10K\
    ZGZfbW9udGhseV9jaGVjay5kcm9wKGNvbHVtbnM9Wyd0aXAnLCAnZmFyZSddLCBpbnBsYWNlPVRydWUp')
    eval(compile(decoded_code, '<string>', 'exec'))

    tc.assertEqual(df_monthly.shape, df_monthly_check.shape, 'Incorrect number of rows and/or columns')
    pd.testing.assert_frame_equal(
        df_monthly.sort_values(['year', 'month']).reset_index(drop=True),
        df_monthly_check.sort_values(['year', 'month']).reset_index(drop=True),
        check_like=True
    )
except Exception as err:
    did_pass = False
    message = err
    
    if not is_autograder_env:
        raise err
finally:
    if is_autograder_env:
        record_part(part_name, did_pass, available_points, message)

{'netid': 'grader_netid', 'order': ['deliverable-01', 'deliverable-02', 'deliverable-03', 'deliverable-04', 'deliverable-05', 'deliverable-06', 'deliverable-07', 'deliverable-08'], 'student_score': 20, 'total_available': 20, 'breakdown': {'deliverable-01': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-02': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-03': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-04': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-05': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}, 'deliverable-06': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-07': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-08': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}}, 'assignment_name': 'case-study-03-ridesharing-trips-dataviz'}

---

🎯 Deliverable 9: Sunburst Chart of Monthly Trips

👇 Tasks

• ✔️ Using df_monthly, create a sunburst chart that shows the proportion of monthly number of trips for both 2019 and 2020.
• ✔️ Set an appropriate title.
• ✔️ Set both the width and height to 600.
• ✔️ Store your figure to a variable named fig.
• ✔️ Display the figure using fig.show()

🔑 Sample Output

🚀 Hint

Replace my_dataframe, 'column1', 'column2', 'column3', and ...s with your own values from the code below.

fig = px.sunburst(
    my_dataframe,
    path=['column1', 'column2'],
    values='column3',
    title='Your Title Here',
    width=...,
    height=...
)
fig.show()

# YOUR CODE BEGINS
fig = px.sunburst(
    df_monthly,
    path=['year', 'month'],
    values='num_trips',
    title='Trips Breakdown by Year and Month',
    width=600,
    height=600
)

fig.show()
# YOUR CODE ENDS

🧭 Check Your Work

• Once you're done, run the code cell below to test correctness.
• ✔️ If the code cell runs without an error, you're good to move on.
• ❌ If the code cell throws an error, go back and fix incorrect parts.

# DO NOT CHANGE THE CODE IN THIS CELL
part_name = "deliverable-09"
did_pass = True
available_points = 3
message = ""

try:
    tc.assertIsNotNone(fig.layout.title.text, 'Missing figure title')
    tc.assertEqual(fig.data[0].type, 'sunburst', 'Must be a sunburst chart')
    tc.assertEqual(fig.layout.width, 600, 'Incorrect width')
    tc.assertEqual(fig.layout.height, 600, 'Incorrect height')

    decoded_code = base64.b64decode(b'ZmlnX2NoZWNrID0gcHguc3VuYnVyc3QoCiAgICBkZ\
    l9tb250aGx5X2NoZWNrLAogICAgcGF0aD1bJ3llYXInLCAnbW9udGgnXSwKICAgIHZhbHVlcz0n\
    bnVtX3RyaXBzJywKICAgIHRpdGxlPSdUcmlwcyBCcmVha2Rvd24gYnkgWWVhciBhbmQgTW9udGg\
    nLAogICAgd2lkdGg9NjAwLAogICAgaGVpZ2h0PTYwMAop')
    eval(compile(decoded_code, '<string>', 'exec'))

    np.testing.assert_array_equal(
        fig.data[0].labels,
        fig_check.data[0].labels,
        'Label(s) mismatch'
    )

    np.testing.assert_array_equal(
        fig.data[0].parents,
        fig_check.data[0].parents,
        'Parent(s) mismatch'
    )

    np.testing.assert_array_equal(
        fig.data[0].values,
        fig_check.data[0].values,
        'Value(s) mismatch'
    )
except Exception as err:
    did_pass = False
    message = err
    
    if not is_autograder_env:
        raise err
finally:
    if is_autograder_env:
        record_part(part_name, did_pass, available_points, message)

{'netid': 'grader_netid', 'order': ['deliverable-01', 'deliverable-02', 'deliverable-03', 'deliverable-04', 'deliverable-05', 'deliverable-06', 'deliverable-07', 'deliverable-08', 'deliverable-09'], 'student_score': 23, 'total_available': 23, 'breakdown': {'deliverable-01': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-02': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-03': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-04': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-05': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}, 'deliverable-06': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-07': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-08': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-09': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}}, 'assignment_name': 'case-study-03-ridesharing-trips-dataviz'}

---

🎯 Deliverable 10: Monthly number of trips in 2019 and 2020 (facet grid bar chart)

👇 Tasks

• ✔️ Using df_monthly, create two bar charts side-by-side within a same figure showing the number of monthly trips for 2019 and 2020.
  • You will have to use a facet grid that we haven't covered in lectures.
  • A facet grid is a group of plots within one figure categorized by 1 or 2 criteria.
  • This deliverable expects you to do some research on your own, although the hint will provide majority of the code required to complete this task.
• ✔️ Set an appropriate title.
• ✔️ Use the 'plotly_dark' theme.
• ✔️ Set the width to 1000 and height to 500.
• ✔️ Store your figure to a variable named fig.
• ✔️ Display the figure using fig.show()

🔑 Sample Output

🚀 Hint

Replace my_dataframe, 'column1', 'column2', 'column3', and ...s with your own values from the code below.

fig = px.bar(
    my_dataframe,
    title='Your Title Here',
    x='column1',
    y='column2',
    facet_col='column3',
    width=...,
    height=...,
    template='plotly_dark',
    color='num_trips',
    color_continuous_scale=['White', 'Yellow']
)
fig.show()

# YOUR CODE BEGINS
fig = px.bar(
    df_monthly,
    title='Monthly Number of Trips in 2019 and 2020',
    x='month',
    y='num_trips',
    facet_col='year',
    width=1000,
    height=500,
    template='plotly_dark',
    color='num_trips',
    color_continuous_scale=['White', 'Yellow']
)
fig.show()
# YOUR CODE ENDS

🧭 Check Your Work

• Once you're done, run the code cell below to test correctness.
• ✔️ If the code cell runs without an error, you're good to move on.
• ❌ If the code cell throws an error, go back and fix incorrect parts.

# DO NOT CHANGE THE CODE IN THIS CELL
part_name = "deliverable-10"
did_pass = True
available_points = 5
message = ""

try:
    tc.assertIsNotNone(fig.layout.title.text, 'Missing figure title')
    tc.assertEqual(len(fig.data), 2, 'Must use a facet grid to display two bar charts side-by-side')
    tc.assertEqual(fig.layout.width, 1000, 'Incorrect width')
    tc.assertEqual(fig.layout.height, 500, 'Incorrect height')
    tc.assertEqual(fig.layout.template, pio.templates['plotly_dark'], 'Incorrect plotly theme (template)')

    decoded_code = base64.b64decode(b'ZmlnX2NoZWNrID0gcHguYmFyKAogICAgZGZfbW9udGhseV9jaGV\
    jaywKICAgIHRpdGxlPSdNb250aGx5IE51bWJlciBvZiBUcmlwcyBpbiAyMDE5IGFuZCAyMDIwJywKICAgIHg9\
    J21vbnRoJywKICAgIHk9J251bV90cmlwcycsCiAgICBmYWNldF9jb2w9J3llYXInLAogICAgd2lkdGg9MTAwM\
    CwKICAgIGhlaWdodD01MDAsCiAgICB0ZW1wbGF0ZT0ncGxvdGx5X2RhcmsnLAogICAgY29sb3I9J251bV90cm\
    lwcycsCiAgICBjb2xvcl9jb250aW51b3VzX3NjYWxlPVsnV2hpdGUnLCAnWWVsbG93J10KKQ==')
    eval(compile(decoded_code, '<string>', 'exec'))

    for i in range(len(fig_check.data)):
        tc.assertEqual(fig.data[i].type, 'bar', 'Must be a bar chart')
        tc.assertEqual(fig.data[i].orientation, 'v', 'Must be a vertical bar chart')

        np.testing.assert_array_equal(
            fig.data[i].x,
            fig_check.data[i].x,
            'x-axis value(s) mismatch'
        )

        np.testing.assert_array_equal(
            fig.data[i].y,
            fig_check.data[i].y,
            'y-axis value(s) mismatch'
        )
except Exception as err:
    did_pass = False
    message = err
    
    if not is_autograder_env:
        raise err
finally:
    if is_autograder_env:
        record_part(part_name, did_pass, available_points, message)

{'netid': 'grader_netid', 'order': ['deliverable-01', 'deliverable-02', 'deliverable-03', 'deliverable-04', 'deliverable-05', 'deliverable-06', 'deliverable-07', 'deliverable-08', 'deliverable-09', 'deliverable-10'], 'student_score': 28, 'total_available': 28, 'breakdown': {'deliverable-01': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-02': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-03': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-04': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-05': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}, 'deliverable-06': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-07': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-08': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-09': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-10': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}}, 'assignment_name': 'case-study-03-ridesharing-trips-dataviz'}

---

🎯 Deliverable 11: Monthly number of trips in 2019 and 2020 (line plot)

👇 Tasks

• ✔️ Using df_monthly, create a line plot of monthly number of trips.
  • Use year to create two separate lines (encoded by different colors) for 2019 and 2020.
• ✔️ Set an appropriate title.
• ✔️ Use the 'plotly_dark' theme.
• ✔️ Set the width to 800 and height to 500.
• ✔️ Store your figure to a variable named fig.
• ✔️ Display the figure using fig.show()

🔑 Sample Output

🚀 Hint

Replace my_dataframe, 'column1', 'column2', 'column3', and ...s with your own values from the code below.

fig = px.line(
    my_dataframe,
    title='Your Title Here',
    x='column1',
    y='column2',
    color='column3',
    template='plotly_dark',
    width=...,
    height=...
)
fig.show()

# YOUR CODE BEGINS
fig = px.line(
    df_monthly,
    title='Monthly Number of Trips Comparison between 2019 and 2020',
    x='month',
    y='num_trips',
    color='year',
    template='plotly_dark',
    width=800,
    height=500
)

fig.show()
# YOUR CODE ENDS

🧭 Check Your Work

• Once you're done, run the code cell below to test correctness.
• ✔️ If the code cell runs without an error, you're good to move on.
• ❌ If the code cell throws an error, go back and fix incorrect parts.

# DO NOT CHANGE THE CODE IN THIS CELL
part_name = "deliverable-11"
did_pass = True
available_points = 4
message = ""

try:
    tc.assertIsNotNone(fig.layout.title.text, 'Missing figure title')
    tc.assertEqual(len(fig.data), df_monthly_check['year'].nunique(), 'Must encode each year with different colors')
    tc.assertEqual(fig.layout.width, 800, 'Incorrect width')
    tc.assertEqual(fig.layout.height, 500, 'Incorrect height')
    tc.assertEqual(fig.layout.template, pio.templates['plotly_dark'], 'Incorrect plotly theme (template)')

    decoded_code = base64.b64decode(b'ZmlnX2NoZWNrID0gcHgubGluZSgKICAgIG\
    RmX21vbnRobHlfY2hlY2ssCiAgICB0aXRsZT0nTW9udGhseSBOdW1iZXIgb2YgVHJpcH\
    MgQ29tcGFyaXNvbiBiZXR3ZWVuIDIwMTkgYW5kIDIwMjAnLAogICAgeD0nbW9udGgnLA\
    ogICAgeT0nbnVtX3RyaXBzJywKICAgIGNvbG9yPSd5ZWFyJywKICAgIHRlbXBsYXRlPS\
    dwbG90bHlfZGFyaycsCiAgICB3aWR0aD04MDAsCiAgICBoZWlnaHQ9NTAwCik=')
    eval(compile(decoded_code, '<string>', 'exec'))

    for i in range(len(fig_check.data)):
        # In plotly, a line plot is a scatter plot with lines connecting the dots
        tc.assertEqual(fig.data[i].type, 'scatter', 'Must be a line plot')
        tc.assertIsNotNone(fig_check.data[i].line.color, 'Must be a line plot')

        np.testing.assert_array_equal(
            fig.data[i].x,
            fig_check.data[i].x,
            'x-axis value(s) mismatch'
        )

        np.testing.assert_array_equal(
            fig.data[i].y,
            fig_check.data[i].y,
            'y-axis value(s) mismatch'
        )
except Exception as err:
    did_pass = False
    message = err
    
    if not is_autograder_env:
        raise err
finally:
    if is_autograder_env:
        record_part(part_name, did_pass, available_points, message)

{'netid': 'grader_netid', 'order': ['deliverable-01', 'deliverable-02', 'deliverable-03', 'deliverable-04', 'deliverable-05', 'deliverable-06', 'deliverable-07', 'deliverable-08', 'deliverable-09', 'deliverable-10', 'deliverable-11'], 'student_score': 32, 'total_available': 32, 'breakdown': {'deliverable-01': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-02': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-03': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-04': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-05': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}, 'deliverable-06': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-07': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-08': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-09': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-10': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-11': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}}, 'assignment_name': 'case-study-03-ridesharing-trips-dataviz'}

---

📌 Interpreting the pre-covid vs post-covid monthly number of trips

• 🔍 In March 2020, the number of trips have plummeted due to the widespread outbreak of COVID-19.
• 🔍 April 2020 was the worst month for ridesharing drivers.
• 🔍 The number of trips have slowly increased from April to October.
• 🔍 In November 2020, continued surge in the number of COVID-19 cases has caused the number of trips to go down again.

---

💵 Part 4: Visualize the effect of COVID-19 on tips

Did passengers tip more on average during the pandemic since they appreciated the drivers providing services during risky times?

Or did the passengers tip less on average since the pandemic has devastated the nation's economy in 2020?

---

🎯 Deliverable 12: Monthly average tip percentages in 2019 and 2020 (facet grid bar chart)

👇 Tasks

• ✔️ Using df_monthly, create two bar charts side-by-side within a same figure showing the monthly average tip percentages in 2019 and 2020.
  • tip_pct column in df_monthly contains the monthly average tip percentages.
• ✔️ Set an appropriate title.
• ✔️ Use the 'plotly_dark' theme.
• ✔️ Set the width to 1000 and height to 500.
• ✔️ Store your figure to a variable named fig.
• ✔️ Display the figure using fig.show()

🔑 Sample Output

🚀 Hint

Replace my_dataframe, 'column1', 'column2', 'column3', and ...s with your own values from the code below.

fig = px.bar(
    my_dataframe,
    title='Your Title Here',
    x='column1',
    y='column2',
    facet_col='column3',
    width=...,
    height=...,
    template='plotly_dark',
    color='tip_pct',
    color_continuous_scale=['White', 'GreenYellow']
)
fig.update_layout(yaxis_tickformat='%')
fig.update_layout(yaxis2_tickformat='%')
fig.show()

# YOUR CODE BEGINS
fig = px.bar(
    df_monthly,
    title='Monthly Average Tip % Based on Fares in 2019 and 2020',
    x='month',
    y='tip_pct',
    facet_col='year',
    width=1000,
    height=500,
    template='plotly_dark',
    color='tip_pct',
    color_continuous_scale=['White', 'GreenYellow']
)
fig.update_layout(yaxis_tickformat='%')
fig.update_layout(yaxis2_tickformat='%')
fig.show()
# YOUR CODE ENDS

🧭 Check Your Work

• Once you're done, run the code cell below to test correctness.
• ✔️ If the code cell runs without an error, you're good to move on.
• ❌ If the code cell throws an error, go back and fix incorrect parts.

# DO NOT CHANGE THE CODE IN THIS CELL
part_name = "deliverable-12"
did_pass = True
available_points = 5
message = ""

try:
    tc.assertIsNotNone(fig.layout.title.text, 'Missing figure title')
    tc.assertEqual(len(fig.data), 2, 'Must use a facet grid to display two bar charts side-by-side')
    tc.assertEqual(fig.layout.width, 1000, 'Incorrect width')
    tc.assertEqual(fig.layout.height, 500, 'Incorrect height')
    tc.assertEqual(fig.layout.template, pio.templates['plotly_dark'], 'Incorrect plotly theme (template)')

    decoded_code = base64.b64decode(b'ZmlnX2NoZWNrID0gcHguYmFyKAogICAgZGZfbW9udGhseV9\
    jaGVjaywKICAgIHRpdGxlPSdNb250aGx5IEF2ZXJhZ2UgVGlwICUgQmFzZWQgb24gRmFyZXMgaW4gMjAx\
    OSBhbmQgMjAyMCcsCiAgICB4PSdtb250aCcsCiAgICB5PSd0aXBfcGN0JywKICAgIGZhY2V0X2NvbD0ne\
    WVhcicsCiAgICB3aWR0aD0xMDAwLAogICAgaGVpZ2h0PTUwMCwKICAgIHRlbXBsYXRlPSdwbG90bHlfZG\
    FyaycsCiAgICBjb2xvcj0ndGlwX3BjdCcsCiAgICBjb2xvcl9jb250aW51b3VzX3NjYWxlPVsnV2hpdGU\
    nLCAnR3JlZW5ZZWxsb3cnXQop')
    eval(compile(decoded_code, '<string>', 'exec'))

    for i in range(len(fig_check.data)):
        tc.assertEqual(fig.data[i].type, 'bar', 'Must be a bar chart')
        tc.assertEqual(fig.data[i].orientation, 'v', 'Must be a vertical bar chart')

        np.testing.assert_array_equal(
            fig.data[i].x,
            fig_check.data[i].x,
            'x-axis value(s) mismatch'
        )

        np.testing.assert_array_equal(
            fig.data[i].y,
            fig_check.data[i].y,
            'y-axis value(s) mismatch'
        )
except Exception as err:
    did_pass = False
    message = err
    
    if not is_autograder_env:
        raise err
finally:
    if is_autograder_env:
        record_part(part_name, did_pass, available_points, message)

{'netid': 'grader_netid', 'order': ['deliverable-01', 'deliverable-02', 'deliverable-03', 'deliverable-04', 'deliverable-05', 'deliverable-06', 'deliverable-07', 'deliverable-08', 'deliverable-09', 'deliverable-10', 'deliverable-11', 'deliverable-12'], 'student_score': 37, 'total_available': 37, 'breakdown': {'deliverable-01': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-02': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-03': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-04': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-05': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}, 'deliverable-06': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-07': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-08': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-09': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-10': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-11': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}, 'deliverable-12': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}}, 'assignment_name': 'case-study-03-ridesharing-trips-dataviz'}

---

🎯 Deliverable 13: Monthly average tip percentages in 2019 and 2020

👇 Tasks

• ✔️ Using df_monthly, create a line plot of monthly average tip percentages.
  • Use year to create two separate lines (encoded by different colors) for 2019 and 2020.
  • tip_pct column in df_monthly contains the monthly average tip percentages.
• ✔️ Set an appropriate title.
• ✔️ Use the 'plotly_dark' theme.
• ✔️ Set the width to 800 and height to 500.
• ✔️ Store your figure to a variable named fig.
• ✔️ Display the figure using fig.show()

🔑 Sample Output

🚀 Hint

Replace my_dataframe, 'column1', 'column2', 'column3', and ...s with your own values from the code below.

fig = px.line(
    my_dataframe,
    title='Your Title Here',
    x='column1',
    y='column2',
    color='column3',
    template='plotly_dark',
    width=...,
    height=...
)
fig.update_layout(yaxis_tickformat='%')
fig.show()

# YOUR CODE BEGINS
fig = px.line(
    df_monthly,
    title='Monthly Average Tip % Comparison between 2019 and 2020',
    x='month',
    y='tip_pct',
    color='year',
    template='plotly_dark',
    width=800,
    height=500
)
fig.update_layout(yaxis_tickformat='%')
fig.show()
# YOUR CODE ENDS

🧭 Check Your Work

• Once you're done, run the code cell below to test correctness.
• ✔️ If the code cell runs without an error, you're good to move on.
• ❌ If the code cell throws an error, go back and fix incorrect parts.

# DO NOT CHANGE THE CODE IN THIS CELL
part_name = "deliverable-13"
did_pass = True
available_points = 4
message = ""

try:
    tc.assertIsNotNone(fig.layout.title.text, 'Missing figure title')
    tc.assertEqual(len(fig.data), df_monthly_check['year'].nunique(), 'Must encode each year with different colors')
    tc.assertEqual(fig.layout.width, 800, 'Incorrect width')
    tc.assertEqual(fig.layout.height, 500, 'Incorrect height')
    tc.assertEqual(fig.layout.template, pio.templates['plotly_dark'], 'Incorrect plotly theme (template)')

    decoded_code = base64.b64decode(b'ZmlnX2NoZWNrID0gcHgubGluZSgKICAgIGRmX21vbnRob\
    HlfY2hlY2ssCiAgICB0aXRsZT0nTW9udGhseSBBdmVyYWdlIFRpcCAlIENvbXBhcmlzb24gYmV0d2Vl\
    biAyMDE5IGFuZCAyMDIwJywKICAgIHg9J21vbnRoJywKICAgIHk9J3RpcF9wY3QnLAogICAgY29sb3I\
    9J3llYXInLAogICAgdGVtcGxhdGU9J3Bsb3RseV9kYXJrJywKICAgIHdpZHRoPTgwMCwKICAgIGhlaWdodD01MDAKKQ==')
    eval(compile(decoded_code, '<string>', 'exec'))

    for i in range(len(fig_check.data)):
        # In plotly, a line plot is a scatter plot with lines connecting the dots
        tc.assertEqual(fig.data[i].type, 'scatter', 'Must be a line plot')
        tc.assertIsNotNone(fig_check.data[i].line.color, 'Must be a line plot')

        np.testing.assert_array_equal(
            fig.data[i].x,
            fig_check.data[i].x,
            'x-axis value(s) mismatch'
        )

        np.testing.assert_array_equal(
            fig.data[i].y,
            fig_check.data[i].y,
            'y-axis value(s) mismatch'
        )
except Exception as err:
    did_pass = False
    message = err
    
    if not is_autograder_env:
        raise err
finally:
    if is_autograder_env:
        record_part(part_name, did_pass, available_points, message)

{'netid': 'grader_netid', 'order': ['deliverable-01', 'deliverable-02', 'deliverable-03', 'deliverable-04', 'deliverable-05', 'deliverable-06', 'deliverable-07', 'deliverable-08', 'deliverable-09', 'deliverable-10', 'deliverable-11', 'deliverable-12', 'deliverable-13'], 'student_score': 41, 'total_available': 41, 'breakdown': {'deliverable-01': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-02': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-03': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-04': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-05': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}, 'deliverable-06': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-07': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-08': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-09': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-10': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-11': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}, 'deliverable-12': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-13': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}}, 'assignment_name': 'case-study-03-ridesharing-trips-dataviz'}

---

📌 Interpreting the pre-covid vs post-covid average percentage of tips

• 🔍 Passengers tipped less after the COVID-19 outbreak.
• 🔍 Possible explanations:
  • COVID-19 has caused an economic downturn.
  • Many people have also lost jobs during this period.
  • COVID-19 may have prevented the driver and the passenger(s) from having a conversation. Less conversation could result in less tips.

---

✨ Part 5: Trips right before July 4th fireworks

Every July 4th, massive crowds gather around places (or boats) to view the fireworks. How did July 4th become the "national fireworks day"? July 4, 1776 is considered to be the birth of United States of Amercia as an independent nation. The Continental Congress approved the final wording of the Declaration of Independence on July 4, 1776. The first-ever recorded July 4th fireworks celebration was held in Philadelphia on July 4, 1777. Since then, there hasn't been an Independence Day without a firework. 💥💥

[Source 1] [Source 2]

In this part, you will find trips started on July 4th between 5-6 PM and create different scatter plots based on those trips.

---

🎯 Deliverable 14: Filter July 4th 5-6 PM trips

👇 Tasks

• ✔️ Using df, filter rows where:
  • month is 7 (July) and,
  • day is 4 (4th) and,
  • hour is 17 (All trips started between 5-6 pm)
• ✔️ Store the filtered DataFrame to a new variable named df_july_fourth.
• ✔️ df should remain unaltered after running your code.

🚀 Hint

The code below filters rows where column is 10, column2 is 20, and column3 is 30. The filtered DataFrame is stored to a new variable named my_filtered.

my_filtered = my_df[(my_df['column1'] == 10) & (my_df['column2'] == 20) & (my_df['column3'] == 30)]

# YOUR CODE BEGINS
df_july_fourth = df[(df['month'] == 7) & (df['day'] == 4) & (df['hour'] == 17)]
# YOUR CODE ENDS

display(df_july_fourth.sample(3))

	start	trip_seconds	trip_miles	pickup_area	dropoff_area	fare	tip	additional_charges	trip_total	shared_trip_authorized	trips_pooled	pickup_lat	pickup_lon	dropoff_lat	dropoff_lon	year	month	day	dayofweek	hour	weekday_weekend
497046	2019-07-04 17:30:00	1259	7.2	Near South Side	Lake View	12.5	0.0	2.55	15.05	False	1	41.859350	-87.617358	41.946295	-87.654298	2019	7	4	3	17	weekday
496959	2019-07-04 17:00:00	442	2.2	Loop	Armour Square	5.0	0.0	2.55	7.55	False	1	41.880994	-87.632746	41.851018	-87.635092	2019	7	4	3	17	weekday
497077	2019-07-04 17:45:00	384	1.6	Loop	Loop	5.0	0.0	2.55	7.55	False	1	41.880994	-87.632746	41.880994	-87.632746	2019	7	4	3	17	weekday

🧭 Check Your Work

• Once you're done, run the code cell below to test correctness.
• ✔️ If the code cell runs without an error, you're good to move on.
• ❌ If the code cell throws an error, go back and fix incorrect parts.

# DO NOT CHANGE THE CODE IN THIS CELL
part_name = "deliverable-14"
did_pass = True
available_points = 3
message = ""

try:
    decoded_code = base64.b64decode(b'ZGZfanVseV9mb3VydGhfY2hlY2sgPSBkZl9iYWNrdXBbKGRmX2JhY2t1cFs\
    nbW9udGgnXSA9PSA3KSAmIChkZl9iYWNrdXBbJ2RheSddID09IDQpICYgKGRmX2JhY2t1cFsnaG91ciddID09IDE3KV0=')
    eval(compile(decoded_code, '<string>', 'exec'))

    tc.assertEqual(df_july_fourth.shape, df_july_fourth_check.shape, 'Incorrect number of rows and/or columns')
    pd.testing.assert_frame_equal(
        df_july_fourth.sort_values(df_july_fourth.columns.tolist()).reset_index(drop=True),
        df_july_fourth_check.sort_values(df_july_fourth_check.columns.tolist()).reset_index(drop=True),
        check_like=True
    )
except Exception as err:
    did_pass = False
    message = err
    
    if not is_autograder_env:
        raise err
finally:
    if is_autograder_env:
        record_part(part_name, did_pass, available_points, message)

{'netid': 'grader_netid', 'order': ['deliverable-01', 'deliverable-02', 'deliverable-03', 'deliverable-04', 'deliverable-05', 'deliverable-06', 'deliverable-07', 'deliverable-08', 'deliverable-09', 'deliverable-10', 'deliverable-11', 'deliverable-12', 'deliverable-13', 'deliverable-14'], 'student_score': 44, 'total_available': 44, 'breakdown': {'deliverable-01': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-02': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-03': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-04': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-05': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}, 'deliverable-06': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-07': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-08': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-09': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-10': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-11': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}, 'deliverable-12': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-13': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}, 'deliverable-14': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}}, 'assignment_name': 'case-study-03-ridesharing-trips-dataviz'}

---

🎯 Deliverable 15: July 4th 5-6 PM trips scatter plots

👇 Tasks

• ✔️ Using df_july_fourth, create two scatter plots for each year side-by-side within a same figure with the following axes.
  • x: trip_seconds,
  • y: trip_miles,
• ✔️ Use the trip_total column to differente the sizes of points.
• ✔️ Use the shared_trip_authorized column to differente the colors of points.
• ✔️ Set an appropriate title.
• ✔️ Use the 'plotly_dark' theme.
• ✔️ Set the width to 1200 and height to 500.
• ✔️ Store your figure to a variable named fig.
• ✔️ Display the figure using fig.show()

🔑 Sample Output

🚀 Hint

Replace my_dataframe, 'column1', 'column2', 'column3', 'column4', 'column5', and ...s with your own values from the code below.

fig = px.scatter(
    my_dataframe,
    title='Your Title Here',
    x='column1',
    y='column2',
    size='column3',
    facet_col='column4',
    color='column5',
    template='plotly_dark',
    width=...,
    height=...,
)
fig.show()

# YOUR CODE BEGINS
fig = px.scatter(
    df_july_fourth,
    title='Trip Seconds vs Trip Miles with July 4th 5-6 PM Trips',
    x='trip_seconds',
    y='trip_miles',
    size='trip_total',
    facet_col='year',
    color='shared_trip_authorized',
    template='plotly_dark',
    width=1200,
    height=500,
)
fig.show()
# YOUR CODE ENDS

🧭 Check Your Work

• Once you're done, run the code cell below to test correctness.
• ✔️ If the code cell runs without an error, you're good to move on.
• ❌ If the code cell throws an error, go back and fix incorrect parts.

# DO NOT CHANGE THE CODE IN THIS CELL
part_name = "deliverable-15"
did_pass = True
available_points = 5
message = ""

try:
    decoded_code = base64.b64decode(b'ZmlnX2NoZWNrID0gcHguc2NhdHRlcigKICAgIGRmX2p1bHlf\
    Zm91cnRoX2NoZWNrLAogICAgdGl0bGU9J1RyaXAgU2Vjb25kcyB2cyBUcmlwIE1pbGVzIHdpdGggSnVseS\
    A0dGggNS02IFBNIFRyaXBzJywKICAgIHg9J3RyaXBfc2Vjb25kcycsCiAgICB5PSd0cmlwX21pbGVzJywK\
    ICAgIHNpemU9J3RyaXBfdG90YWwnLAogICAgZmFjZXRfY29sPSd5ZWFyJywKICAgIGNvbG9yPSdzaGFyZW\
    RfdHJpcF9hdXRob3JpemVkJywKICAgIHRlbXBsYXRlPSdwbG90bHlfZGFyaycsCiAgICB3aWR0aD0xMjAw\
    LAogICAgaGVpZ2h0PTUwMCwKKQ==')
    eval(compile(decoded_code, '<string>', 'exec'))


    # DO NOT CHANGE THE CODE IN THIS CELL
    tc.assertIsNotNone(fig.layout.title.text, 'Missing figure title')
    tc.assertEqual(len(fig.data), len(fig_check.data), 
                   'Must use a facet grid to display two scatter plots side-by-side with color-encoding')
    tc.assertEqual(fig.layout.width, 1200, 'Incorrect width')
    tc.assertEqual(fig.layout.height, 500, 'Incorrect height')
    tc.assertEqual(fig.layout.template, pio.templates['plotly_dark'], 'Incorrect plotly theme (template)')

    for i in range(len(fig_check.data)):
        tc.assertEqual(fig.data[i].type, 'scatter', 'Must be a scatter plot')

        np.testing.assert_array_equal(
            fig.data[i].x,
            fig_check.data[i].x,
            'x-axis value(s) mismatch'
        )

        np.testing.assert_array_equal(
            fig.data[i].y,
            fig_check.data[i].y,
            'y-axis value(s) mismatch'
        )
except Exception as err:
    did_pass = False
    message = err
    
    if not is_autograder_env:
        raise err
finally:
    if is_autograder_env:
        record_part(part_name, did_pass, available_points, message)

{'netid': 'grader_netid', 'order': ['deliverable-01', 'deliverable-02', 'deliverable-03', 'deliverable-04', 'deliverable-05', 'deliverable-06', 'deliverable-07', 'deliverable-08', 'deliverable-09', 'deliverable-10', 'deliverable-11', 'deliverable-12', 'deliverable-13', 'deliverable-14', 'deliverable-15'], 'student_score': 49, 'total_available': 49, 'breakdown': {'deliverable-01': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-02': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-03': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-04': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-05': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}, 'deliverable-06': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-07': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-08': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-09': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-10': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-11': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}, 'deliverable-12': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-13': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}, 'deliverable-14': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-15': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}}, 'assignment_name': 'case-study-03-ridesharing-trips-dataviz'}

---

📌 Interpreting the pre-covid vs post-covid scatter plots

• 🔍 There is a positive linear relationship between trip_seconds and trip_miles.
• 🔍 The number of trips made on July 4th has decreased signifcantly in 2020 due to COVID-19.
• 🔍 There are no pooled trips (shared_trips_authorized == True) in 2020.
  • Why? Uber and Lyft both suspended pooled rides to limit the spread of coronavirus (Reuters article here) beginning in March 2020.

---

🎯 Deliverable 16: July 4th 5-6 PM trips 3D scatter plots (Pre-COVID)

👇 Tasks

• ✔️ Using df_july_fourth, create a 3D scatter plot of trips in 2019 using the following axes.
  • x: trip_seconds,
  • y: trip_miles,
  • z: trip_total
• ✔️ Use the shared_trip_authorized column to differente the colors of points.
• ✔️ Set an appropriate title.
• ✔️ Use the 'plotly_dark' theme.
• ✔️ Set both the width and height to 800.
• ✔️ Store your figure to a variable named fig.
• ✔️ Display the figure using fig.show()

🔑 Sample Output

🚀 Hint

Replace 'column1', 'column2', 'column3', 'column4', and ...s with your own values from the code below.

fig = px.scatter_3d(
    df_july_fourth[df_july_fourth['year'] == 2019],
    title='Your Title Here',
    x='column1',
    y='column2',
    z='column3',
    color='column4',
    template='plotly_dark',
    width=...,
    height=...
)
fig.show()

# YOUR CODE BEGINS
fig = px.scatter_3d(
    df_july_fourth[df_july_fourth['year'] == 2019],
    title='Trip Seconds vs Trip Miles vs Trip Total with July 4th, 2019 5-6 PM Trips',
    x='trip_seconds',
    y='trip_miles',
    z='trip_total',
    color='shared_trip_authorized',
    template='plotly_dark',
    width=800,
    height=800
)
fig.show()
# YOUR CODE ENDS

🧭 Check Your Work

• Once you're done, run the code cell below to test correctness.
• ✔️ If the code cell runs without an error, you're good to move on.
• ❌ If the code cell throws an error, go back and fix incorrect parts.

# DO NOT CHANGE THE CODE IN THIS CELL
part_name = "deliverable-16"
did_pass = True
available_points = 5
message = ""

try:
    decoded_code = base64.b64decode(b'ZmlnX2NoZWNrID0gcHguc2NhdHRl\
    cl8zZCgKICAgIGRmX2p1bHlfZm91cnRoX2NoZWNrW2RmX2p1bHlfZm91cnRoX2\
    NoZWNrWyd5ZWFyJ10gPT0gMjAxOV0sCiAgICB0aXRsZT0nVHJpcCBTZWNvbmRz\
    IHZzIFRyaXAgTWlsZXMgdnMgVHJpcCBUb3RhbCB3aXRoIEp1bHkgNHRoLCAyMD\
    E5IDUtNiBQTSBUcmlwcycsCiAgICB4PSd0cmlwX3NlY29uZHMnLAogICAgeT0n\
    dHJpcF9taWxlcycsCiAgICB6PSd0cmlwX3RvdGFsJywKICAgIGNvbG9yPSdzaG\
    FyZWRfdHJpcF9hdXRob3JpemVkJywKICAgIHRlbXBsYXRlPSdwbG90bHlfZGFy\
    aycsCiAgICB3aWR0aD04MDAsCiAgICBoZWlnaHQ9ODAwCik=')
    eval(compile(decoded_code, '<string>', 'exec'))


    # DO NOT CHANGE THE CODE IN THIS CELL
    tc.assertIsNotNone(fig.layout.title.text, 'Missing figure title')
    tc.assertEqual(len(fig.data), len(fig_check.data), "Check whether you've supplied the color parameter to px.scatter_3d()")
    tc.assertEqual(fig.layout.width, 800, 'Incorrect width')
    tc.assertEqual(fig.layout.height, 800, 'Incorrect height')
    tc.assertEqual(fig.layout.template, pio.templates['plotly_dark'], 'Incorrect plotly theme (template)')

    for i in range(len(fig_check.data)):
        tc.assertEqual(fig.data[i].type, 'scatter3d', 'Must be a 3D scatter plot')

        np.testing.assert_array_equal(
            fig.data[i].x,
            fig_check.data[i].x,
            'x-axis value(s) mismatch'
        )

        np.testing.assert_array_equal(
            fig.data[i].y,
            fig_check.data[i].y,
            'y-axis value(s) mismatch'
        )

        np.testing.assert_array_equal(
            fig.data[i].z,
            fig_check.data[i].z,
            'z-axis value(s) mismatch'
        )
except Exception as err:
    did_pass = False
    message = err
    
    if not is_autograder_env:
        raise err
finally:
    if is_autograder_env:
        record_part(part_name, did_pass, available_points, message)

{'netid': 'grader_netid', 'order': ['deliverable-01', 'deliverable-02', 'deliverable-03', 'deliverable-04', 'deliverable-05', 'deliverable-06', 'deliverable-07', 'deliverable-08', 'deliverable-09', 'deliverable-10', 'deliverable-11', 'deliverable-12', 'deliverable-13', 'deliverable-14', 'deliverable-15', 'deliverable-16'], 'student_score': 54, 'total_available': 54, 'breakdown': {'deliverable-01': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-02': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-03': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-04': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-05': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}, 'deliverable-06': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-07': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-08': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-09': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-10': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-11': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}, 'deliverable-12': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-13': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}, 'deliverable-14': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-15': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-16': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}}, 'assignment_name': 'case-study-03-ridesharing-trips-dataviz'}

▶️ Run the code cell below to repeat the previous deliverable with trips in 2020.

# DO NOT CHANGE THE CODE BELOW
fig = px.scatter_3d(
    df_july_fourth[df_july_fourth['year'] == 2020],
    title='Trip Seconds vs Trip Miles vs Trip Total with July 4th, 2020 5-6 PM Trips',
    x='trip_seconds',
    y='trip_miles',
    z='trip_total',
    color='shared_trip_authorized',
    template='plotly_dark',
    width=800,
    height=800
)
fig.show()

---

📌 Interpreting the pre-covid vs post-covid 3D scatter plots

• 🔍 There is a positive linear relationship between trip_seconds, trip_miles, and trip_total.
• 🔍 You may have to rotate the view of your 3D scatter plots to witness the linear relationships.

---

🏡 Part 6: Pickup/dropff area analysis

In this part, you will find the top 20 pickup areas and analyze the trips originating from those areas.

---

🎯 Deliverable 17: Find top 20 pickup areas by number of trips

👇 Tasks

• ✔️ Find the top 20 pickup areas in df_listings by number of listings.
• ✔️ Store the result to a new variable named top_20_pickup_areas.
• ✔️ top_20_pickup_areas should be a Python list type.
• ✔️ We'll give you the fully-working code below.

🔥 Solution

# YOUR CODE BEGINS
top_20_pickup_areas = df['pickup_area'].value_counts().head(20).index.tolist()
# YOUR CODE ENDS

print(top_20_pickup_areas)

['Near North Side', 'Loop', 'Near West Side', 'Lake View', 'West Town', 'Lincoln Park', 'Logan Square', 'Uptown', 'Ohare', 'Near South Side', 'Edgewater', 'Hyde Park', 'Lower West Side', 'North Center', 'Austin', 'Avondale', 'Lincoln Square', 'Rogers Park', 'Irving Park', 'South Shore']

🧭 Check your work

• Once you're done, run the code cell below to test correctness.
• ✔️ If the code cell runs without an error, you're good to move on.
• ❌ If the code cell throws an error, go back and fix incorrect parts.

# DO NOT CHANGE THE CODE IN THIS CELL
part_name = "deliverable-17"
did_pass = True
available_points = 2
message = ""

try:
    decoded_code = base64.b64decode(b'dG9wXzIwX3BpY2t1cF9hcmVhc19jaGVjayA9IGRmX2JhY\
    2t1cFsncGlja3VwX2FyZWEnXS52YWx1ZV9jb3VudHMoKS5oZWFkKDIwKS5pbmRleC50b2xpc3QoKQ==')
    eval(compile(decoded_code, '<string>', 'exec'))

    tc.assertEqual(set(top_20_pickup_areas), set(top_20_pickup_areas_check), 'Incorrect pickup areas')
except Exception as err:
    did_pass = False
    message = err
    
    if not is_autograder_env:
        raise err
finally:
    if is_autograder_env:
        record_part(part_name, did_pass, available_points, message)

{'netid': 'grader_netid', 'order': ['deliverable-01', 'deliverable-02', 'deliverable-03', 'deliverable-04', 'deliverable-05', 'deliverable-06', 'deliverable-07', 'deliverable-08', 'deliverable-09', 'deliverable-10', 'deliverable-11', 'deliverable-12', 'deliverable-13', 'deliverable-14', 'deliverable-15', 'deliverable-16', 'deliverable-17'], 'student_score': 56, 'total_available': 56, 'breakdown': {'deliverable-01': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-02': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-03': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-04': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-05': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}, 'deliverable-06': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-07': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-08': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-09': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-10': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-11': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}, 'deliverable-12': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-13': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}, 'deliverable-14': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-15': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-16': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-17': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}}, 'assignment_name': 'case-study-03-ridesharing-trips-dataviz'}

---

🎯 Deliverable 18: Filter trips from top 20 pickup areas

👇 Tasks

• ✔️ Using df, filter only the rows where the pickup_area is in top_20_pickup_areas.
• ✔️ Store the filtered result to a new variable named df_filtered.

🚀 Hint

filtered_dataframe = my_dataframe[(my_dataframe['column1'].isin(my_top_10_list))]

# YOUR CODE BEGINS
df_filtered = df[(df['pickup_area'].isin(top_20_pickup_areas))]
# YOUR CODE ENDS

display(df_filtered.head(3))
print(f'There are {df_filtered.shape[0]} rows and {df_filtered.shape[1]} columns in the filtered DataFrame')

	start	trip_seconds	trip_miles	pickup_area	dropoff_area	fare	tip	additional_charges	trip_total	shared_trip_authorized	trips_pooled	pickup_lat	pickup_lon	dropoff_lat	dropoff_lon	year	month	day	dayofweek	hour	weekday_weekend
1	2019-01-01	697	3.0	Near North Side	Near West Side	7.5	0.0	2.5	10.0	False	1	41.892073	-87.628874	41.885300	-87.642808	2019	1	1	1	0	weekday
2	2019-01-01	1598	4.7	Lincoln Park	Loop	10.0	2.0	2.5	14.5	False	1	41.922083	-87.634156	41.870607	-87.622173	2019	1	1	1	0	weekday
3	2019-01-01	573	0.9	Near North Side	Near North Side	5.0	0.0	2.5	7.5	False	1	41.892042	-87.631864	41.892508	-87.626215	2019	1	1	1	0	weekday

There are 1084442 rows and 21 columns in the filtered DataFrame

🧭 Check your work

• Once you're done, run the code cell below to test correctness.
• ✔️ If the code cell runs without an error, you're good to move on.
• ❌ If the code cell throws an error, go back and fix incorrect parts.

# DO NOT CHANGE THE CODE IN THIS CELL
part_name = "deliverable-18"
did_pass = True
available_points = 3
message = ""

try:
    decoded_code = base64.b64decode(b'ZGZfZmlsdGVyZWRfY2hlY2sgPSBkZl9iYWNrdXBbKGR\
    mX2JhY2t1cFsncGlja3VwX2FyZWEnXS5pc2luKHRvcF8yMF9waWNrdXBfYXJlYXNfY2hlY2spKV0=')
    eval(compile(decoded_code, '<string>', 'exec'))

    tc.assertEqual(df_filtered.shape, df_filtered_check.shape, 'Incorrect number of rows and/or columns')
except Exception as err:
    did_pass = False
    message = err
    
    if not is_autograder_env:
        raise err
finally:
    if is_autograder_env:
        record_part(part_name, did_pass, available_points, message)

{'netid': 'grader_netid', 'order': ['deliverable-01', 'deliverable-02', 'deliverable-03', 'deliverable-04', 'deliverable-05', 'deliverable-06', 'deliverable-07', 'deliverable-08', 'deliverable-09', 'deliverable-10', 'deliverable-11', 'deliverable-12', 'deliverable-13', 'deliverable-14', 'deliverable-15', 'deliverable-16', 'deliverable-17', 'deliverable-18'], 'student_score': 59, 'total_available': 59, 'breakdown': {'deliverable-01': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-02': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-03': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-04': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-05': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}, 'deliverable-06': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-07': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-08': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-09': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-10': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-11': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}, 'deliverable-12': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-13': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}, 'deliverable-14': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-15': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-16': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-17': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-18': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}}, 'assignment_name': 'case-study-03-ridesharing-trips-dataviz'}

---

🎯 Deliverable 19: Number of trips and average trip total by pickup_area

👇 Tasks

• ✔️ Using df_filtered, calculate the following aggregated values by pickup_area.
  • num_trips: Number of trips
  • trip_total: Average trip_total
• ✔️ Store the resulting DataFrame to a new variable named df_by_pickup_area.
• ✔️ We'll give you the fully-working code below.

🔥 Solution

# YOUR CODE BEGINS
df_by_pickup_area = df_filtered.groupby(
    'pickup_area', as_index=False
).agg({
    'start': 'count',
    'trip_total': 'mean',
}).rename(columns={
    'start': 'num_trips'
})
# YOUR CODE ENDS

display(df_by_pickup_area.head(5))

	pickup_area	num_trips	trip_total
0	Austin	16851	13.246273
1	Avondale	16425	12.623803
2	Edgewater	21689	13.867470
3	Hyde Park	21303	14.523999
4	Irving Park	14570	13.326660

🧭 Check your work

• Once you're done, run the code cell below to test correctness.
• ✔️ If the code cell runs without an error, you're good to move on.
• ❌ If the code cell throws an error, go back and fix incorrect parts.

# DO NOT CHANGE THE CODE IN THIS CELL
part_name = "deliverable-19"
did_pass = True
available_points = 2
message = ""

try:
    decoded_code = base64.b64decode(b'ZGZfYnlfcGlja3VwX2FyZWFfY2hlY2sgPSBkZl9maWx0Z\
    XJlZF9jaGVjay5ncm91cGJ5KAogICAgJ3BpY2t1cF9hcmVhJywgYXNfaW5kZXg9RmFsc2UKKS5hZ2co\
    ewogICAgJ3N0YXJ0JzogJ2NvdW50JywKICAgICd0cmlwX3RvdGFsJzogJ21lYW4nLAp9KS5yZW5hbWU\
    oY29sdW1ucz17CiAgICAnc3RhcnQnOiAnbnVtX3RyaXBzJwp9KQ==')
    eval(compile(decoded_code, '<string>', 'exec'))

    tc.assertEqual(
        df_by_pickup_area.shape,
        df_by_pickup_area_check.shape,
        'Incorrect number of rows and/or columns'
    )
    pd.testing.assert_frame_equal(
        df_by_pickup_area.sort_values(df_by_pickup_area.columns.tolist()).reset_index(drop=True),
        df_by_pickup_area_check.sort_values(df_by_pickup_area_check.columns.tolist()).reset_index(drop=True),
        check_like=True
    )
except Exception as err:
    did_pass = False
    message = err
    
    if not is_autograder_env:
        raise err
finally:
    if is_autograder_env:
        record_part(part_name, did_pass, available_points, message)

{'netid': 'grader_netid', 'order': ['deliverable-01', 'deliverable-02', 'deliverable-03', 'deliverable-04', 'deliverable-05', 'deliverable-06', 'deliverable-07', 'deliverable-08', 'deliverable-09', 'deliverable-10', 'deliverable-11', 'deliverable-12', 'deliverable-13', 'deliverable-14', 'deliverable-15', 'deliverable-16', 'deliverable-17', 'deliverable-18', 'deliverable-19'], 'student_score': 61, 'total_available': 61, 'breakdown': {'deliverable-01': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-02': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-03': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-04': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-05': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}, 'deliverable-06': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-07': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-08': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-09': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-10': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-11': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}, 'deliverable-12': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-13': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}, 'deliverable-14': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-15': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-16': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-17': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-18': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-19': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}}, 'assignment_name': 'case-study-03-ridesharing-trips-dataviz'}

---

🎯 Deliverable 20: Number of trips by pickup area (bar chart)

👇 Tasks

• ✔️ Using df_by_pickup_area, create a horizontal bar chart displaying the number of trips by each pickup area.
• ✔️ Set an appropriate title.
• ✔️ Set the height to 800 and do not supply a width (by default, a Plotly figure will expand to fit the window if the width parameter is omitted).
• ✔️ Use the plotly_white template.
• ✔️ Store your figure to a variable named fig.
• ✔️ Display the figure using fig.show()

🔑 Sample Output

🚀 Hint

Replace my_dataframe, 'column1', 'column2', and ...s with your own values from the code below.

fig = px.bar(
    my_dataframe,
    title='Your Title Here',
    x='column1',
    y='column2',
    color='column1',
    color_continuous_scale='emrld',
    text='column1',
    template='plotly_white',
    height=...
)
fig.update_traces(texttemplate='%{text:.2s}', textposition='outside')
fig.update_yaxes(categoryorder='total ascending')
fig.show()

# YOUR CODE BEGINS
fig = px.bar(
    df_by_pickup_area,
    title='Number of Trips by Pickup Area',
    x='num_trips',
    y='pickup_area',
    color='num_trips',
    color_continuous_scale='emrld',
    text='num_trips',
    template='plotly_white',
    height=800
)
fig.update_traces(texttemplate='%{text:.2s}', textposition='outside')
fig.update_yaxes(categoryorder='total ascending')
fig.show()
# YOUR CODE ENDS

🧭 Check Your Work

• Once you're done, run the code cell below to test correctness.
• ✔️ If the code cell runs without an error, you're good to move on.
• ❌ If the code cell throws an error, go back and fix incorrect parts.

# DO NOT CHANGE THE CODE IN THIS CELL
part_name = "deliverable-20"
did_pass = True
available_points = 5
message = ""

try:
    tc.assertIsNotNone(fig.layout.title.text, 'Missing figure title')
    tc.assertEqual(fig.layout.height, 800, 'Incorrect height')
    tc.assertEqual(fig.layout.template, pio.templates['plotly_white'], 'Incorrect plotly theme (template)')

    decoded_code = base64.b64decode(b'ZmlnX2NoZWNrID0gcHguYmFyKAog\
    ICAgZGZfYnlfcGlja3VwX2FyZWFfY2hlY2ssCiAgICB0aXRsZT0nTnVtYmVyIG\
    9mIFRyaXBzIGJ5IFBpY2t1cCBBcmVhJywKICAgIHg9J251bV90cmlwcycsCiAg\
    ICB5PSdwaWNrdXBfYXJlYScsCiAgICBjb2xvcj0nbnVtX3RyaXBzJywKICAgIG\
    NvbG9yX2NvbnRpbnVvdXNfc2NhbGU9J2VtcmxkJywKICAgIHRleHQ9J251bV90\
    cmlwcycsCiAgICB0ZW1wbGF0ZT0ncGxvdGx5X3doaXRlJywKICAgIGhlaWdodD04MDAKKQ==')
    eval(compile(decoded_code, '<string>', 'exec'))

    tc.assertEqual(fig.data[0].type, 'bar', 'Must be a line plot')

    np.testing.assert_array_equal(
        fig.data[0].x,
        fig_check.data[0].x,
        'x-axis value(s) mismatch'
    )

    np.testing.assert_array_equal(
        fig.data[0].y,
        fig_check.data[0].y,
        'y-axis value(s) mismatch'
    )
except Exception as err:
    did_pass = False
    message = err
    
    if not is_autograder_env:
        raise err
finally:
    if is_autograder_env:
        record_part(part_name, did_pass, available_points, message)

{'netid': 'grader_netid', 'order': ['deliverable-01', 'deliverable-02', 'deliverable-03', 'deliverable-04', 'deliverable-05', 'deliverable-06', 'deliverable-07', 'deliverable-08', 'deliverable-09', 'deliverable-10', 'deliverable-11', 'deliverable-12', 'deliverable-13', 'deliverable-14', 'deliverable-15', 'deliverable-16', 'deliverable-17', 'deliverable-18', 'deliverable-19', 'deliverable-20'], 'student_score': 66, 'total_available': 66, 'breakdown': {'deliverable-01': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-02': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-03': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-04': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-05': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}, 'deliverable-06': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-07': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-08': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-09': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-10': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-11': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}, 'deliverable-12': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-13': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}, 'deliverable-14': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-15': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-16': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-17': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-18': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-19': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-20': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}}, 'assignment_name': 'case-study-03-ridesharing-trips-dataviz'}

---

🎯 Deliverable 21: Average trip total by pickup area (bar chart)

👇 Tasks

• ✔️ Using df_by_pickup_area, create a horizontal bar chart displaying the average trip total by each pickup area.
• ✔️ Set an appropriate title.
• ✔️ Set the height to 800 and do not supply a width (by default, a Plotly figure will expand to fit the window if the width parameter is omitted).
• ✔️ Use the plotly_white template.
• ✔️ Store your figure to a variable named fig.
• ✔️ Display the figure using fig.show()

🔑 Sample Output

🚀 Hint

Replace my_dataframe, 'column1', 'column2', and ...s with your own values from the code below.

fig = px.bar(
    my_dataframe,
    title='Your Title Here',
    x='column1',
    y='column2',
    text='column1',
    template='plotly_white',
    height=...
)
fig.update_traces(texttemplate='$%{text:.1f}', textposition='outside')
fig.update_yaxes(categoryorder='total ascending')
fig.show()

# YOUR CODE BEGINS
fig = px.bar(
    df_by_pickup_area,
    title='Average Trip Total ($) by Pickup Area',
    x='trip_total',
    y='pickup_area',
    text='trip_total',
    template='plotly_white',
    height=800
)
fig.update_traces(texttemplate='$%{text:.1f}', textposition='outside')
fig.update_yaxes(categoryorder='total ascending')
fig.show()
# YOUR CODE ENDS

🧭 Check Your Work

• Once you're done, run the code cell below to test correctness.
• ✔️ If the code cell runs without an error, you're good to move on.
• ❌ If the code cell throws an error, go back and fix incorrect parts.

# DO NOT CHANGE THE CODE IN THIS CELL
part_name = "deliverable-21"
did_pass = True
available_points = 5
message = ""

try:
    tc.assertIsNotNone(fig.layout.title.text, 'Missing figure title')
    tc.assertEqual(fig.layout.height, 800, 'Incorrect height')
    tc.assertEqual(fig.layout.template, pio.templates['plotly_white'], 'Incorrect plotly theme (template)')

    decoded_code = base64.b64decode(b'ZmlnX2NoZWNrID0gcHguYmFyKAogICAg\
    ZGZfYnlfcGlja3VwX2FyZWFfY2hlY2ssCiAgICB0aXRsZT0nQXZlcmFnZSBUcmlwIF\
    RvdGFsICgkKSBieSBQaWNrdXAgQXJlYScsCiAgICB4PSd0cmlwX3RvdGFsJywKICAg\
    IHk9J3BpY2t1cF9hcmVhJywKICAgIHRleHQ9J3RyaXBfdG90YWwnLAogICAgdGVtcG\
    xhdGU9J3Bsb3RseV93aGl0ZScsCiAgICBoZWlnaHQ9ODAwCik=')
    eval(compile(decoded_code, '<string>', 'exec'))

    tc.assertEqual(fig.data[0].type, 'bar', 'Must be a line plot')

    np.testing.assert_array_equal(
        fig.data[0].x,
        fig_check.data[0].x,
        'x-axis value(s) mismatch'
    )

    np.testing.assert_array_equal(
        fig.data[0].y,
        fig_check.data[0].y,
        'y-axis value(s) mismatch'
    )
except Exception as err:
    did_pass = False
    message = err
    
    if not is_autograder_env:
        raise err
finally:
    if is_autograder_env:
        record_part(part_name, did_pass, available_points, message)

{'netid': 'grader_netid', 'order': ['deliverable-01', 'deliverable-02', 'deliverable-03', 'deliverable-04', 'deliverable-05', 'deliverable-06', 'deliverable-07', 'deliverable-08', 'deliverable-09', 'deliverable-10', 'deliverable-11', 'deliverable-12', 'deliverable-13', 'deliverable-14', 'deliverable-15', 'deliverable-16', 'deliverable-17', 'deliverable-18', 'deliverable-19', 'deliverable-20', 'deliverable-21'], 'student_score': 71, 'total_available': 71, 'breakdown': {'deliverable-01': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-02': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-03': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-04': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-05': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}, 'deliverable-06': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-07': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-08': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-09': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-10': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-11': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}, 'deliverable-12': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-13': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}, 'deliverable-14': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-15': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-16': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-17': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-18': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-19': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-20': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-21': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}}, 'assignment_name': 'case-study-03-ridesharing-trips-dataviz'}

---

🎯 Deliverable 22: Pickup area breakdown (treemap)

👇 Tasks

• ✔️ Using df_by_pickup_area, create a treemap of pickup areas.
• ✔️ Set an appropriate title.
• ✔️ Store your figure to a variable named fig.
• ✔️ Display the figure using fig.show()

🔑 Sample Output

🚀 Hint

Replace my_dataframe, 'column1', and 'column2' with your own values from the code below.

fig = px.treemap(
    my_dataframe,
    title='Pickup Area Breakdown',
    path=['column1'],
    values='column2',
    height=600
)
fig.show()

# YOUR CODE BEGINS
fig = px.treemap(
    df_by_pickup_area,
    title='Pickup Area Breakdown',
    path=['pickup_area'],
    values='num_trips',
    height=600
)
fig.show()
# YOUR CODE ENDS

🧭 Check Your Work

• Once you're done, run the code cell below to test correctness.
• ✔️ If the code cell runs without an error, you're good to move on.
• ❌ If the code cell throws an error, go back and fix incorrect parts.

# DO NOT CHANGE THE CODE IN THIS CELL
part_name = "deliverable-22"
did_pass = True
available_points = 5
message = ""

try:
    tc.assertIsNotNone(fig.layout.title.text, 'Missing figure title')

    decoded_code = base64.b64decode(b'ZmlnX2NoZWNrID0gcHgudHJlZW1hcCg\
    KICAgIGRmX2J5X3BpY2t1cF9hcmVhX2NoZWNrLAogICAgdGl0bGU9J1BpY2t1cCBB\
    cmVhIEJyZWFrZG93bicsCiAgICBwYXRoPVsncGlja3VwX2FyZWEnXSwKICAgIHZhb\
    HVlcz0nbnVtX3RyaXBzJywKICAgIGhlaWdodD02MDAKKQ==')
    eval(compile(decoded_code, '<string>', 'exec'))

    tc.assertEqual(fig.data[0].type, 'treemap', 'Must be a treemap')

    np.testing.assert_array_equal(
        fig.data[0].labels,
        fig_check.data[0].labels,
        'Label value(s) mismatch'
    )

    np.testing.assert_array_equal(
        fig.data[0].parents,
        fig_check.data[0].parents,
        'Parent value(s) mismatch'
    )

    np.testing.assert_array_equal(
        fig.data[0].values,
        fig_check.data[0].values,
        'Size value(s) mismatch'
    )
except Exception as err:
    did_pass = False
    message = err
    
    if not is_autograder_env:
        raise err
finally:
    if is_autograder_env:
        record_part(part_name, did_pass, available_points, message)

{'netid': 'grader_netid', 'order': ['deliverable-01', 'deliverable-02', 'deliverable-03', 'deliverable-04', 'deliverable-05', 'deliverable-06', 'deliverable-07', 'deliverable-08', 'deliverable-09', 'deliverable-10', 'deliverable-11', 'deliverable-12', 'deliverable-13', 'deliverable-14', 'deliverable-15', 'deliverable-16', 'deliverable-17', 'deliverable-18', 'deliverable-19', 'deliverable-20', 'deliverable-21', 'deliverable-22'], 'student_score': 76, 'total_available': 76, 'breakdown': {'deliverable-01': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-02': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-03': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-04': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-05': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}, 'deliverable-06': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-07': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-08': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-09': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-10': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-11': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}, 'deliverable-12': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-13': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}, 'deliverable-14': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-15': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-16': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-17': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-18': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-19': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-20': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-21': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-22': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}}, 'assignment_name': 'case-study-03-ridesharing-trips-dataviz'}

---

📌 Interpreting the pickup area visualizations

• 🔍 "Near North Side" is the most popular pickup area, followed by "Loop" and "Near West Side".
• 🔍 The average trip total for rides beginning in Ohare is relatively much higher compared to any other pickup area.

---

🦄 Part 7: Pickup area + weekday/weekend

In this part, you will add a new dimension (weekday/weekend) to the top 20 pickup areas.

▶️ Run the code cell below to create the number of trips and average trip totals by pickup area and weekday/weekend classification.

# DO NOT CHANGE THE CODE BELOW
df_by_pickup_area_weekday_weekend = df_filtered.groupby(
    ['pickup_area', 'weekday_weekend'], as_index=False
).agg({
    'start': 'count',
    'trip_total': 'mean',
}).rename(columns={
    'start': 'num_trips'
})

display(df_by_pickup_area_weekday_weekend.head(5))

	pickup_area	weekday_weekend	num_trips	trip_total
0	Austin	weekday	9064	13.213864
1	Austin	weekend	7787	13.283996
2	Avondale	weekday	7942	12.617546
3	Avondale	weekend	8483	12.629662
4	Edgewater	weekday	10745	14.131752

---

🎯 Deliverable 23: Pickup area + weekday/weekend breakdown (sunburst)

👇 Tasks

• ✔️ Copy the code below to create a sunburst chart of number of trips by pickup area + weekday/weekend

🔥 Solution

# YOUR CODE BEGINS
fig = px.sunburst(
    df_by_pickup_area_weekday_weekend,
    path=['pickup_area', 'weekday_weekend'],
    values='num_trips',
    title='Pickup Area Breakdown (Weekday/Weekend)',
    width=800,
    height=800
)
fig.show()
# YOUR CODE ENDS

# DO NOT CHANGE THE CODE IN THIS CELL
part_name = "deliverable-23"
did_pass = True
available_points = 2
message = ""

try:
    tc.assertIsNotNone(fig.layout.title.text, 'Missing figure title')
    tc.assertEqual(fig.data[0].type, 'sunburst', 'Must be a sunburst chart')
    tc.assertEqual(fig.layout.width, 800, 'Incorrect width')
    tc.assertEqual(fig.layout.height, 800, 'Incorrect height')

    decoded_code = base64.b64decode(b'ZmlnX2NoZWNrID0gcHguc3VuYnVyc3\
    QoCiAgICBkZl9ieV9waWNrdXBfYXJlYV93ZWVrZGF5X3dlZWtlbmQsCiAgICBwYX\
    RoPVsncGlja3VwX2FyZWEnLCAnd2Vla2RheV93ZWVrZW5kJ10sCiAgICB2YWx1ZX\
    M9J251bV90cmlwcycsCiAgICB0aXRsZT0nUGlja3VwIEFyZWEgQnJlYWtkb3duIC\
    hXZWVrZGF5L1dlZWtlbmQpJywKICAgIHdpZHRoPTgwMCwKICAgIGhlaWdodD04MDAKKQ==')
    eval(compile(decoded_code, '<string>', 'exec'))

    np.testing.assert_array_equal(
        fig.data[0].labels,
        fig_check.data[0].labels,
        'Label(s) mismatch'
    )

    np.testing.assert_array_equal(
        fig.data[0].parents,
        fig_check.data[0].parents,
        'Parent(s) mismatch'
    )

    np.testing.assert_array_equal(
        fig.data[0].values,
        fig_check.data[0].values,
        'Value(s) mismatch'
    )
except Exception as err:
    did_pass = False
    message = err
    
    if not is_autograder_env:
        raise err
finally:
    if is_autograder_env:
        record_part(part_name, did_pass, available_points, message)

{'netid': 'grader_netid', 'order': ['deliverable-01', 'deliverable-02', 'deliverable-03', 'deliverable-04', 'deliverable-05', 'deliverable-06', 'deliverable-07', 'deliverable-08', 'deliverable-09', 'deliverable-10', 'deliverable-11', 'deliverable-12', 'deliverable-13', 'deliverable-14', 'deliverable-15', 'deliverable-16', 'deliverable-17', 'deliverable-18', 'deliverable-19', 'deliverable-20', 'deliverable-21', 'deliverable-22', 'deliverable-23'], 'student_score': 78, 'total_available': 78, 'breakdown': {'deliverable-01': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-02': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-03': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-04': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-05': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}, 'deliverable-06': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-07': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-08': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-09': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-10': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-11': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}, 'deliverable-12': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-13': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}, 'deliverable-14': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-15': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-16': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-17': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-18': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-19': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-20': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-21': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-22': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-23': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}}, 'assignment_name': 'case-study-03-ridesharing-trips-dataviz'}

---

🎯 Deliverable 24: Pickup area + weekday/weekend breakdown (treemap)

👇 Tasks

• ✔️ Copy the code below to create a treemap chart of number of trips by pickup area + weekday/weekend

🔥 Solution

# YOUR CODE BEGINS
fig = px.treemap(
    df_by_pickup_area_weekday_weekend,
    path=['pickup_area', 'weekday_weekend'],
    values='num_trips',
    title='Pickup Area Breakdown (Weekday/Weekend)',
    height=600
)
fig.show()
# YOUR CODE ENDS

# DO NOT CHANGE THE CODE IN THIS CELL
part_name = "deliverable-24"
did_pass = True
available_points = 2
message = ""

try:
    tc.assertIsNotNone(fig.layout.title.text, 'Missing figure title')
    tc.assertEqual(fig.data[0].type, 'treemap', 'Must be a treemap chart')
    tc.assertEqual(fig.layout.height, 600, 'Incorrect height')

    decoded_code = base64.b64decode(b'ZmlnID0gcHgudHJlZW1hcCgKICAgIGRm\
    X2J5X3BpY2t1cF9hcmVhX3dlZWtkYXlfd2Vla2VuZCwKICAgIHBhdGg9WydwaWNrdX\
    BfYXJlYScsICd3ZWVrZGF5X3dlZWtlbmQnXSwKICAgIHZhbHVlcz0nbnVtX3RyaXBz\
    JywKICAgIHRpdGxlPSdQaWNrdXAgQXJlYSBCcmVha2Rvd24gKFdlZWtkYXkvV2Vla2\
    VuZCknLAogICAgaGVpZ2h0PTYwMAop')
    eval(compile(decoded_code, '<string>', 'exec'))

    np.testing.assert_array_equal(
        fig.data[0].labels,
        fig_check.data[0].labels,
        'Label(s) mismatch'
    )

    np.testing.assert_array_equal(
        fig.data[0].parents,
        fig_check.data[0].parents,
        'Parent(s) mismatch'
    )

    np.testing.assert_array_equal(
        fig.data[0].values,
        fig_check.data[0].values,
        'Value(s) mismatch'
    )
except Exception as err:
    did_pass = False
    message = err
    
    if not is_autograder_env:
        raise err
finally:
    if is_autograder_env:
        record_part(part_name, did_pass, available_points, message)

{'netid': 'grader_netid', 'order': ['deliverable-01', 'deliverable-02', 'deliverable-03', 'deliverable-04', 'deliverable-05', 'deliverable-06', 'deliverable-07', 'deliverable-08', 'deliverable-09', 'deliverable-10', 'deliverable-11', 'deliverable-12', 'deliverable-13', 'deliverable-14', 'deliverable-15', 'deliverable-16', 'deliverable-17', 'deliverable-18', 'deliverable-19', 'deliverable-20', 'deliverable-21', 'deliverable-22', 'deliverable-23', 'deliverable-24'], 'student_score': 80, 'total_available': 80, 'breakdown': {'deliverable-01': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-02': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-03': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-04': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-05': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}, 'deliverable-06': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-07': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-08': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-09': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-10': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-11': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}, 'deliverable-12': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-13': {'available_points': 4, 'points': 4, 'did_pass': True, 'message': ''}, 'deliverable-14': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-15': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-16': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-17': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-18': {'available_points': 3, 'points': 3, 'did_pass': True, 'message': ''}, 'deliverable-19': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-20': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-21': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-22': {'available_points': 5, 'points': 5, 'did_pass': True, 'message': ''}, 'deliverable-23': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}, 'deliverable-24': {'available_points': 2, 'points': 2, 'did_pass': True, 'message': ''}}, 'assignment_name': 'case-study-03-ridesharing-trips-dataviz'}

---

🍸 Submitting your notebook

There is one final step before exporting the notebook as an .ipynb file for submission. You should restart your runtime (kernal) and run all cells from the beginning to ensure that your notebook is structured properly.

Go to the "Runtime" ("Kernel" if you're on Jupyter Lab) menu on top. Select "Restart and run all". Failing to pass this step may result in significant loss of points since the autograder will fail to run.

print('🎯 Restart and run all successful')

🎯 Restart and run all successful

# GRADER_ONLY
import os
from datetime import datetime
autograder_output_filename = f"{assignment_name}-autograder-results.csv"

netid = autograded_result["netid"]
assignment_name = autograded_result["assignment_name"]
student_score = autograded_result["student_score"]
total_available = autograded_result["total_available"]
graded_time = datetime.now().strftime("%Y-%m-%d %H:%M")
feedback = '''Assignment: {0}
NetID: {1}
Total Score: {2}/{3}

Grade Breakdown'''.format(assignment_name, netid, student_score, total_available)

for graded_part_name in autograded_result["order"]:
    part_info = autograded_result["breakdown"][graded_part_name]
    feedback += '''
--------------------------------
Component: {0}
Result: {1}
Score: {2}/{3}'''.format(graded_part_name, "Pass" if part_info["did_pass"] else "Fail", part_info["points"], part_info["available_points"])

    feedback += "" if part_info["did_pass"] else f"\nError Message: {part_info['message']}"

if is_autograder_env:
    df_agr_new = pd.DataFrame([{
        "netid": netid,
        "assignment_name": assignment_name,
        "student_score": student_score,
        "total_available": total_available,
        "graded_time": graded_time,
        "feedback": feedback
    }])
    
    if not os.path.exists(autograder_output_filename):
        df_agr = df_agr_new
    else:
        df_agr = pd.read_csv(autograder_output_filename)
        df_agr = df_agr.loc[~((df_agr["netid"] == netid) & (df_agr["assignment_name"] == assignment_name))]
        df_agr = pd.concat([df_agr, df_agr_new])
        
    df_agr.sort_values("graded_time").to_csv(autograder_output_filename, index=None)